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An interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
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Quality Control Using Agent Based Framework

The process of extraction of implicit, previously unknown, and potentially useful knowledge from data. It uses Machine Learning, statistical and visualization techniques to discover and present knowledge in a form that is easily comprehensible to humans. It is a phase in a bigger process: the Knowledge Discovery in Databases (KDD) process.
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Data Mining and the KDD Process

Data mining, also called knowledge discovery in databases, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets.
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A Case-Based-Reasoning System for Feature Selection and Diagnosing Asthma

This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Plan and Rules for Data Analysis Success: A Roadmap

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The application of analytical methods and tools to data for the purpose of identifying patterns, relationships, or obtaining systems that perform useful tasks such as classification, prediction, estimation, or affinity grouping.
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Adaptive Business Intelligence

Nontrivial extraction of implicit, previously unknown and potentially useful information from data. Typically, analytical methods and tools are applied to data with the aim of identifying patterns, relationships or obtaining databases for tasks such as classification, prediction, estimation or clustering
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Neural Network-Based Visual Data Mining for Cancer Data

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The process of selection, exploration, and modeling of large quantities of data to discover regularities or relations that are at first unknown with the aim of obtaining clear and useful results for the owner of the database.
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Combination of Forecasts in Data Mining

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Analysis of data using methods that look for patterns in the data, frequently operating without knowledge of the meaning of the data. Typically, the term is applied to exploration of large-scale databases in contrast to machine-learning methods that are applied to smaller data sets.
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Heuristics in Medical Data Mining

Also known as knowledge discovery in databases (KDD), data mining is the process of automatically searching large volumes of data for patterns. Data mining is a fairly recent and contemporary topic in computing.
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Data Mining: Payoffs and Pitfalls

Mainly concerned with analyzing existing data, typically stored in a database or a data warehouse. It is the core of a knowledge discovery process, which aims at the extraction of interesting, non-trivial, implicit, previously unknown, and potentially useful information from data. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, and data/model visualization.
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A European Virtual Enterprise on Collaborative Data Mining and Decision Support

Important branch in industry and market, retrieving important information from a huge amount of data. It is usually considered with huge amount of heterogeneous data, where the use of computers is inevitable.
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Ant Colonies and Data Mining

Data mining, also called knowledge discovery in databases or knowledge discovery and data mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, and so forth. Data mining is a complex topic, has links with multiple core fields such as computer science, and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning, and pattern recognition.
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Fuzzy Classification in Shipwreck Scatter Analysis

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As a key step in the knowledge discovery from data (KDD) process, it is intended to extract interesting (nontrivial, implicit, previously unknown, and potentially useful) patterns. Inference: The act or process of deriving a conclusion from stored data or known facts. While cognitive psychology studies human inference, automated inference algorithms have been studied in artificial intelligence and in its subfield machine learning.
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Similarity Retrieval and Cluster Analysis Using R* Trees

In common parlance, data mining often refers generally to the idea of probing deeply into some mountain of data. This informal use of the term usually says little about the techniques used to do the probing. In contrast, the more formal use of the term refers specifically to using computational techniques to uncover patterns in huge data sets. Here the techniques range widely from statistics to artificial intelligence. The range of data mining investigations is also varied and ever increasing, but some of the better-known approaches include clustering, classification, and affinity analysis.
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Mining User Activity Data In Higher Education Open Systems: Trends, Challenges, and Possibilities

Data mining, a branch of computer science, is the process of extracting patterns from large data sets by combining statistical analysis and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and business partner selection.
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A Neuro-Fuzzy Partner Selection System for Business Social Networks

Analysis and nontrivial extraction of data from databases for the purpose of discovering new and valuable information, in the form of patterns and rules, from relationships between data elements.
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Process-Based Data Mining

Field of knowledge aimed at the study of techniques to automatically analyse and extract meaningful information from large datasets. There is some overlaping between Data mining and Artificial Intelligences, and some techniques can be included in both research fields (e.g. cluster identification).
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Learning Analytics

Analysis of data in a database using tools which look for trends or anomalies without knowledge of the meaning of the data. The nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The science of extracting useful information from large data sets or databases.
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Association Rules Mining for Retail Organizations

The process through which large amounts of data are sorted with the aim to extract from them relevant information. This term is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods, especially in the biological context. It can be defined as the nontrivial extraction of previously unknown and potentially useful information from data and databases.
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Multi-Level Data Integration and Data Mining in Systems Biology

Sometimes called data or knowledge discovery, data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
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Leadership for Big Data and Business Intelligence

Data mining is an efficient method to process large-scale data and has become a widely used technique in behavior analysis. By data mining, researchers can extract some useful features from massive data. When applied to mobile user behavior analysis, data mining can be helpful in extracting features such as preferences by using the data collected in mobile phones.
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Mobile User Behaviors in China

Data mining is fundamentally a way to search for useful information through large amounts of data being generated with the facility of information technologies. Data mining is also often referred to as ‘analytics’ or ‘knowledge discovery’ because its objective is precisely to generate knowledge or discover patterns of information amongst that data from which useful knowledge can be obtained. The actual mining process is made by software featuring artificial intelligence techniques like ‘machine learning.’ Data mining has become an important complement of other data analysis tools because the amounts of information available these days sometimes are impossible to be analyzed with traditional methods; however it has also become a buzzword which is often miss-used.
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Disruptive Technologies and Education: Is There Any Disruption After All?

Data Mining is a field in knowledge theory that develops and uses tools for retrieving significance from the Big Data. This methodology strives to find a common denominator among some parts of the data. There are two main properties of data: an individual property related to the separate elements of data or a related property of the relationship among some elements of data.
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Visualization and Storage of Big Data for Linguistic Applications

The extraction of non-trivial and actionable patterns from large amounts of data using statistical and artificial intelligence techniques. Directed data mining starts with a question or area of interest, and patterns are sought that answer those needs. Undirected data mining is the use of these tools to explore a dataset for patterns without a guiding research question.
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Bibliomining for Library Decision-Making

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Is the process of autonomously extracting useful information or knowledge from large data stores or sets. Data mining can be performed on a variety of data stores, including the World Wide Web, relational databases, transactional databases, internal legacy systems, pdf documents, and data warehouses.
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Clustering Algorithms for Data Streams

Data mining is a technologically driven process of using algorithms to analyze data from multiple perspectives and extract meaningful patterns that can be used to predict future users behavior The market basket analysis system that Amazon.com uses to recommend new products to its customers on the basis of their past purchases is a widely known example of how data mining can be utilized in marketing.
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Social Media Marketing: Web X.0 of Opportunities

The nontrivial extraction of implicit, previously unknown, and potentially useful information, in the form of useful patterns from data. It is also known under the name of knowledge extraction from large databases, though the two notions are sometimes delicately separated; knowledge discovery usually refers to more formal methods of extracting knowledge.
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Efficient Algorithms for Clustering Data and Text Streams

Is a process of analyzing data from different perspectives and summarizing it into useful information. The information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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Data Mining Applications in Computer-Supported Collaborative Learning

Data Mining refers to extracting or mining knowledge from large amounts of data. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization. Data mining is used to uncover hidden patterns in the underlying data which can be used for decision making process.
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Using the Flipped Classroom to Improve Knowledge Creation of Master's-Level Students in Engineering

This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand, and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Investigating the Factors for Predictive Marketing Implementation in Algerian Organizations

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This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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What the 3Vs Acronym Didn't Put Into Perspective?

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This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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First of All, Understand Data Analytics Context and Changes

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This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Understanding Data Analytics Is Good but Knowing How to Use It Is Better!